Computational Analysis and Acceleration Research Group

At Computational Analysis and Acceleration Research Group (CARG), we develop data-driven solutions to tackle real-world challenges in biomedical, communications and surveillance applications. We combine physical models with machine learning models that account for uncertainty, ensuring our systems are robust and ready for real-life conditions. Our researchers are involved in all stages of the processing pipeline, from experiment design and data collection to data processing and the development of machine learning models for our applications, including UAV detection and inspection, as well as patient data for wearable and contactless medical solutions. To date, more than 70 graduate students and postdoctoral researchers have been part of our multidisciplinary team.

Areas of Focus

• Biomedical & Automotive Innovation We collaborate with medical and industry partners to design signal processing algorithms and machine learning models for patient monitoring and counter-UAS systems.
• Integrated Modelling & Simulation Our team focuses on modeling and simulation to better understand complex physical problems before deploying practical solutions.
• Computational Efficiency We harness hardware accelerators—including GPUs and FPGAs—to achieve real-time performance and scalability.

Why we chose the name CARG

History: this research group evolved from Computer Architecture research group (original CARG). Computer architecture research group was formed in 2008 and focused on the following topics: computer architectures, signal processing architectures, hardware and software accelerators, GPU architectures and programming and algorithmic modifications for more efficient implementation in hardware. In recent years our research has gone in several different directions and therefore we decided to change the name of our group to Computational Analysis and Acceleration Research Group (new CARG).

Computational analysis combines aspects of mathematical modeling, data processing, and algorithm development to produce insights that guide research and practical applications.

Acceleration refers to techniques and technologies used to increase the speed of data processing and computation.